Sökning: "Speaker Diarization"

Visar resultat 1 - 5 av 7 uppsatser innehållade orden Speaker Diarization.

1. 1. Analysis of speaking time and content of the various debates of the presidential campaign : Automated AI analysis of speech time and content of presidential debates based on the audio using speaker detection and topic detection

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Axel Valentin Maza; [2023]
   Nyckelord :Artificial Intelligence; Speaker detection; Speaker recognition; Speaker diarization; Speaker identification; Debate; Politics; Deep Learning; Artificiell intelligens; talardetektion; talarigenkänning; talardiarisering; talaridentifiering; debatt; politik; djupinlärning;

   Sammanfattning : The field of artificial intelligence (AI) has grown rapidly in recent years and its applications are becoming more widespread in various fields, including politics. In particular, presidential debates have become a crucial aspect of election campaigns and it is important to analyze the information exchanged in these debates in an objective way to let voters choose without being influenced by biased data. LÄS MER

3. 2. Speaker diarization in challenging environments using deep networks : An evaluation of a state-of-the-art system

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Mathias Näreaho; [2023]
   Nyckelord :;

   Sammanfattning : Speaker diarization is the task of determining 'who spoke when' in an audio segment. Since the breakthrough of deep learning, speech technology has experienced a huge improvement in a wide range of metrics and fields, and speaker diarization is no different. LÄS MER

4. 3. Estimating the risk of insurance fraud based on tonal analysis

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Henrik Steneld; [2022]
   Nyckelord :Spectral analysis; Speaker recognition; Tonal analysis; Speaker Diarization; Machine Learning; LSTM; ResNet; Fraud detection; Mathematics and Statistics;

   Sammanfattning : Insurance companies utilize various methods for identifying claims that are of potential fraudulent nature. With the ever progressing field of artificial intelligence and machine learning models, great interest can be found within the industry to evaluate the use of new methods that may arise as a result of new advanced models in combination with the rich data that is being gathered. LÄS MER

5. 4. Experiments in speaker diarization using speaker vectors

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Ming Cui; [2021]
   Nyckelord :Speaker Diarization; Embedding Extraction Module; Deep Learning; Supervised method; Unsupervised method; Talardiarisering; inbäddning av extraktionsmodul; djupinlärning; övervakad metod; oövervakad metod;

   Sammanfattning : Speaker Diarization is the task of determining ‘who spoke when?’ in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. It has emerged as an increasingly important and dedicated domain of speech research. LÄS MER

7. 5. Speaker Diarization System for Call-center data

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Yi Li; [2020]
   Nyckelord :MFCC-vector Speaker Diarization; Speaker Verification; Voice Active Detection; Gaussian Mixture Model; Hierarchy Clustering; MFCC-vektor Högtalardarisering; Högtalarverifiering; Röstaktiv detektering; Gaussisk blandningsmodell; Hierarkikluster;

   Sammanfattning : To answer the question who spoke when, speaker diarization (SD) is a critical step for many speech applications in practice. The task of our project is building a MFCC-vector based speaker diarization system on top of a speaker verification system (SV), which is an existing Call-centers application to check the customer’s identity from a phone call. LÄS MER